In this paper, we propose a multipath-assisted device-free localization (DFL) system that includes magnitude and phase information (MAMPI). The DFL system employs ultra-wideband (UWB) channel impulse response (CIR) measurements, enabling the extraction of several multipath components (MPCs) and thereby benefits from multipath propagation. We propose a radio propagation model that calculates the effect on the received signal based on the position of a person within a target area. Additionally, we propose a validated error model for the measurements and explain the creation of different feature vectors and extraction of the MPCs from Decawave DW1000 CIR measurements. We evaluate the system via simulations of the position error probability and a measurement setup in an indoor scenario. We compare the performance of MAMPI to a conventional DFL system based on four sensor nodes that measures radio signal strength values. The combination of the magnitude and phase differences for the feature vectors results in a position error probability that is comparable to a conventional system but requires only two sensor nodes.

In radio-frequency (RF)-based device-free localization (DFL), the number of sensors acting as RF transmitters and receivers is crucial for accuracy and system costs. Two promising approaches for DFL have been identified in the past: radio tomographic imaging (RTI) and multi-static radar (MSR). RTI in its basic version requires many sensors for high accuracy, which increases the cost. In this paper, we show how RTI benefits from multipath propagation. By evaluating the direct and echo paths, we increase the coverage of the target area, and by utilizing UWB signals, the RTI system is less susceptible to multipath propagation. MSR maps reflections that occur within the target area to reflectors such as persons or other objects. MSR does not require that the person is located near a signal path. Both suggested methods exploit ultra-wideband (UWB) channel impulse response (CIR) measurements. CIR measurements and the modeling of multipath effects either increase the accuracy or reduce the required number of sensors for localization with RTI. We created a test setup and measure UWB CIRs at different positions with a commercially available off-the-shelf UWB radio chip, the Decawave DW1000. We compare the localization results of RTI, multipath-assisted (MA)-RTI, and MSR and investigate a combined approach. We show that RTI is improved by the analysis of multipath propagation; furthermore, MA-RTI results in a better performance compared to MSR: with 50% of all cases, the localization error is better than 0.82 m and in 80% of all cases 1.34 m. The combined approach results in the best localization result with 0.64 m in 50% of all cases.

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Device-free localization (DFL) systems exploit changes in the radio frequency channel by measuring, for example, the channel impulse response (CIR), to detect and localize obstacles within a target area. However, due to a lack of well-defined interfaces, missing modularization, as well as complex system configuration, it is difficult to deploy DFL systems outside of laboratory setups. This paper focused on the system view and the challenges that come with setting up a DFL system in an indoor environment. We propose MA-RTI, a modular DFL system that is easy to set up, and which utilizes a multipath-assisted (MA) radio-tomographic imaging (RTI) algorithm. To achieve a modular DFL system, we proposed and implemented an architectural model for DFL systems. For minimizing the configuration overhead, we applied a 3D spatial model, that helps in placing the sensors and calculating the required calibration parameters. Therefore, we configured the system solely with idle measurements and a 3D spatial model. We deployed such a DFL system and evaluated it in a real-world office environment with four sensor nodes. The radio technology was ultra-wideband (UWB) and the corresponding signal measurements were CIRs. The DFL system operated with CIRs that provided a sub-nanosecond time-domain resolution. After pre-processing, the update rate was approximately 46 Hz and it provided a localization accuracy of 1.0 m in 50 % of all cases and 1.8 m in 80 % of all cases. MA fingerprinting approaches lead to higher localization accuracy, but require a labor-intensive training phase.